The DPT [1]} model predicts a disparity map in Euclidean space with an unknown scale \(a\)  and shift \(b\) .
To resolve the scale and shift ambiguity in the predicted disparity maps, we make use of the sparse 3D points extracted by COLMAP.
For a frame at time \(t\) , we first calculate the actual disparity \(1 / \tilde{z}_t\)  of the sparse 3D points by projecting them onto the image, which usually results in sub-pixels.
We then bilinearly interpolate the predicted disparity map to get the predicted disparity \(1 / \tilde{z}^*_t\) .
Scale \(a\)  and shift \(b\)  can then be estimated through linear regression via the relation:
\(\frac{1}{\tilde{z}_t} = a \cdot \frac{1}{\tilde{z}^*_t} + b.\) 
